arXiv subjects

Edi Sutoyo

Publications and source records attributed to Edi Sutoyo.

7 recordsLinked to original sources

arXiv2026

The Dangers of Non-Self-Fixed Architecture Technical Debt and Its Impact on Time-to-Fix

Technical Debt (TD) refers to the long-term costs incurred when developers prioritize short-term delivery over quality-improving work. Architectural Technical Debt (ATD) arises when architectural decisions (e.g., technology choices, patterns, or decomposition) prioritize near-term progress over future maintainability and evolvability. Because ATD affects a system's core structure and propagates through architectural dependencies, it is often more expensive and disruptive to remediate than localized code-level debt. Although ATD has been widely studied, an important but underexplored aspect of repayment is who performs it. Prior work provides limited empirical evidence on repayment responsibility in ATD and its relationship to time-to-fix. We empirically study self-fixed ATD, where the introducer also repays the debt, and contrast it with non-self-fixed ATD in large Apache open-source projects. We reconstruct ATD lifecycles by tracing Jira artifacts to version-control history to identify introduction and repayment points and attribute developer roles. We address three research questions on the prevalence of self-fixed ATD, time-to-fix differences between self-fixed and non--self-fixed items, and how factors related to code change and collaboration metrics relate to repayment speed. Using descriptive statistics, non-parametric tests, and survival analysis, we show that self-fixed and non--self-fixed ATD exhibit distinct repayment dynamics and differences in how changes are shared on ATD-affected files. In particular, non--self-fixed ATD is more likely to remain unresolved longer when changes are spread across many developers. These results provide actionable guidance for maintainers to identify high-risk ATD items and to reduce handoff costs by increasing introducer involvement when possible and documenting the design rationale during repayment.

cs.SE↗

arXiv2026

Reducing Labeling Effort in Architecture Technical Debt Detection through Active Learning and Explainable AI

Self-Admitted Technical Debt (SATD) refers to technical compromises explicitly admitted by developers in natural language artifacts, such as code comments, commit messages, and issue trackers. Among its types, Architecture Technical Debt (ATD) is particularly difficult to detect due to its abstract and context-dependent nature. Manual annotation of ATD is costly, time-consuming, and challenging to scale. To reduce labeling effort, this study combines keyword-based filtering, active learning, and explainable AI for ATD detection. We refined an existing dataset of ATD-related Jira issues to obtain an expert-validated seed set used to extract representative keywords. These keywords were then applied to identify more than 103k candidate issues across 10 open-source projects. To assess the reliability of keyword-based filtering, we qualitatively evaluated a statistically representative sample of labeled issues. Building on the resulting dataset, we applied active learning with multiple query strategies to prioritize informative samples for annotation. The results show that Breaking Ties achieved the best performance, with an F1-score of 0.72 and a 49% reduction in annotation effort. To improve transparency, we used SHAP and LIME to explain ATD classification results. Expert evaluation showed that both methods provided useful explanations, with LIME generally preferred for its clarity and ease of use.

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arXiv2025

Tracing the Lifecycle of Architecture Technical Debt in Software Systems: A Dependency Approach

Architectural technical debt (ATD) represents trade-offs in software architecture that accelerate initial development but create long-term maintenance challenges. ATD, in particular when self-admitted, impacts the foundational structure of software, making it difficult to detect and resolve. This study investigates the lifecycle of ATD, focusing on how it affects i) the connectivity between classes and ii) the frequency of file modifications. We aim to understand how ATD evolves from introduction to repayment and its implications on software architectures. Our empirical approach was applied to a dataset of SATD items extracted from various software artifacts. We isolated ATD instances, filtered for architectural indicators, and calculated dependencies at different lifecycle stages using FAN-IN and FAN-OUT metrics. Statistical analyses, including the Mann-Whitney U test and Cliff's Delta, were used to assess the significance and effect size of connectivity and dependency changes over time. We observed that ATD repayment increased class connectivity, with FAN-IN increasing by 57.5% on average and FAN-OUT by 26.7%, suggesting a shift toward centralization and increased architectural complexity after repayment. Moreover, ATD files were modified less frequently than Non-ATD files, with changes accumulated in high-dependency portions of the code. Our study shows that resolving ATD improves software quality in the short-term, but can make the architecture more complex by centralizing dependencies. Also, even if dependency metrics (like FAN-IN and FAN-OUT) can help understand the impact of ATD, they should be combined with other measures to capture other effects of ATD on software maintainability.

cs.SE↗

arXiv2024

Development and Adoption of SATD Detection Tools: A State-of-practice Report

Self-Admitted Technical Debt (SATD) refers to instances where developers knowingly introduce suboptimal solutions into code and document them, often through textual artifacts. This paper provides a comprehensive state-of-practice report on the development and adoption of SATD detection tools. Through a systematic review of the available literature and tools, we examined their overall accessibility. Our findings reveal that, although SATD detection tools are crucial for maintaining software quality, many face challenges such as technological obsolescence, poor maintenance, and limited platform compatibility. Only a small number of tools are actively maintained, hindering their widespread adoption. This report discusses common anti-patterns in tool development, proposes corrections, and highlights the need for implementing Findable, Accessible, Interoperable, and Reusable (FAIR) principles and fostering greater collaboration between academia and industry to ensure the sustainability and efficacy of these tools. The insights presented here aim to drive more robust management of technical debt and enhance the reliability of SATD tools.

cs.SE↗

arXiv2024

Deep Learning and Data Augmentation for Detecting Self-Admitted Technical Debt

Self-Admitted Technical Debt (SATD) refers to circumstances where developers use textual artifacts to explain why the existing implementation is not optimal. Past research in detecting SATD has focused on either identifying SATD (classifying SATD items as SATD or not) or categorizing SATD (labeling instances as SATD that pertain to requirement, design, code, test debt, etc.). However, the performance of these approaches remains suboptimal, particularly for specific types of SATD, such as test and requirement debt, primarily due to extremely imbalanced datasets. To address these challenges, we build on earlier research by utilizing BiLSTM architecture for the binary identification of SATD and BERT architecture for categorizing different types of SATD. Despite their effectiveness, both architectures struggle with imbalanced data. Therefore, we employ a large language model data augmentation strategy to mitigate this issue. Furthermore, we introduce a two-step approach to identify and categorize SATD across various datasets derived from different artifacts. Our contributions include providing a balanced dataset for future SATD researchers and demonstrating that our approach significantly improves SATD identification and categorization performance compared to baseline methods.

cs.SE↗

arXiv2024

SATDAUG -- A Balanced and Augmented Dataset for Detecting Self-Admitted Technical Debt

Self-admitted technical debt (SATD) refers to a form of technical debt in which developers explicitly acknowledge and document the existence of technical shortcuts, workarounds, or temporary solutions within the codebase. Over recent years, researchers have manually labeled datasets derived from various software development artifacts: source code comments, messages from the issue tracker and pull request sections, and commit messages. These datasets are designed for training, evaluation, performance validation, and improvement of machine learning and deep learning models to accurately identify SATD instances. However, class imbalance poses a serious challenge across all the existing datasets, particularly when researchers are interested in categorizing the specific types of SATD. In order to address the scarcity of labeled data for SATD \textit{identification} (i.e., whether an instance is SATD or not) and \textit{categorization} (i.e., which type of SATD is being classified) in existing datasets, we share the \textit{SATDAUG} dataset, an augmented version of existing SATD datasets, including source code comments, issue tracker, pull requests, and commit messages. These augmented datasets have been balanced in relation to the available artifacts and provide a much richer source of labeled data for training machine learning or deep learning models.

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arXiv2023

Self-Admitted Technical Debt Detection Approaches: A Decade Systematic Review

Technical debt (TD) refers to the long-term costs associated with suboptimal design or code decisions in software development, often made to meet short-term delivery goals. Self-Admitted Technical Debt (SATD) occurs when developers explicitly acknowledge these trade-offs in the codebase, typically through comments or annotations. SATD detection has become an increasingly important research area, particularly with the rise of learning-based techniques that aim to streamline SATD detection. This systematic literature review provides a comprehensive analysis of SATD detection approaches published between 2014 and early 2025, focusing on the evolution of techniques from heuristic-based techniques to more advanced ML, DL, and Transformer-based models. It examines key trends in SATD detection methodologies and tools, evaluates the effectiveness of different approaches using metrics like precision, recall, and F1 score, and highlights the primary challenges in this domain, including dataset heterogeneity, model generalizability, and explainability. The findings reveal that while early heuristic-based techniques laid the foundation for SATD detection, more recent advancements in DL and Transformer models have significantly improved detection accuracy. However, challenges remain in scaling these models for broader industrial adoption. This review offers insights into current research gaps and provides directions for future work, aiming to improve the robustness and practicality of SATD detection tools.

cs.SE↗